3,123 research outputs found
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
Creativity and Machine Learning: a Survey
There is a growing interest in the area of machine learning and creativity.
This survey presents an overview of the history and the state of the art of
computational creativity theories, machine learning techniques, including
generative deep learning, and corresponding automatic evaluation methods. After
presenting a critical discussion of the key contributions in this area, we
outline the current research challenges and emerging opportunities in this
field.Comment: 25 pages, 3 figures, 2 table
Computer analysis of face beauty: a survey
The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and data-driven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: i) relating attractiveness to particular facial features; ii) assessing attractiveness automatically; iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research
Reuse of Neural Modules for General Video Game Playing
A general approach to knowledge transfer is introduced in which an agent
controlled by a neural network adapts how it reuses existing networks as it
learns in a new domain. Networks trained for a new domain can improve their
performance by routing activation selectively through previously learned neural
structure, regardless of how or for what it was learned. A neuroevolution
implementation of this approach is presented with application to
high-dimensional sequential decision-making domains. This approach is more
general than previous approaches to neural transfer for reinforcement learning.
It is domain-agnostic and requires no prior assumptions about the nature of
task relatedness or mappings. The method is analyzed in a stochastic version of
the Arcade Learning Environment, demonstrating that it improves performance in
some of the more complex Atari 2600 games, and that the success of transfer can
be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1
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